Summary
WeightedMovingAverage is RTTA's streaming implementation of: Weighted moving average with larger recent weights.
Update API
result = rtta.WeightedMovingAverage().update(value)
The update(...) call consumes one observation using value. advance(...)
uses the same inputs when the caller wants to update state without materializing
a Python return value.
Theory Of Operation
WeightedMovingAverage is a causal smoother or average. It updates compact rolling or exponential state with the newest observation and returns the current smoothed estimate.
Recurrence
Let \(z_t = value_t\) denote the observation consumed by one
update(...) call and let \(\theta\) denote constructor parameters such as
window lengths, thresholds, and smoothing constants.
The return value is the current scalar indicator value.
Implementation Notes
The recurrence is implemented in src/rtta/indicator.cpp in class WeightedMovingAverage.